Traffic Light Systems with a Visual Monitoring Module

In document Thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy (halaman 37-42)

CHAPTER TWO LITERATURE REVIEW

2.1 Traffic Light Systems

2.1.2 Traffic Light Systems with a Visual Monitoring Module

Kastrinaki et al. (2003) and Padmadas et al. (2010) stated that video-based vehicle detection is a promising solution for traffic surveillance. Recently, it has played an important role in real-time traffic management systems. They added that video-based traffic monitoring systems offer a number of advantages. In addition to vehicle counting, more traffic information can be obtained by video images, including vehicle classifications and lane changes. Furthermore, the researchers stated that video cameras can be easily installed and used in mobile environments.

A computer vision approach has been used widely in classifying vehicles on motorway, measuring vehicles speed and for automatic accident monitoring and intelligent vehicle environment such as, the works by Houben et al. (2009), Kiratiratanapruk and Siddhichai (2009) and Surgailis et al. (2011) respectively. Recently the computer vision approach is used to determine traffic conditions. The next section focuses on previous works related to the goal of this research.

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2.1.2.1 Traffic Light Systems Using a Background Subtraction Monitoring Module Deng et al. (2005) introduced a visual monitoring module for traffic light systems. They used the background subtraction technique to detect vehicles in the street scene. Deng and Lee (2006), Deng et al. (2005a), and Deng et al. (2005b) compared three variants for the same work, which presented the use of vision-based surveillance to keep sight of unpredictable and complex measurable disturbances that perturb the traffic flow. They integrated and performed vision based techniques that embed object segmentation, classification and tracking to compute real time measurements of traffic conditions in urban road. According to the real time traffic measurement, they derived an adaptive traffic signal control algorithm to settle the red–green switching period of traffic lights.

Although the background subtraction method can be used in busy street situations, it has a disadvantage. The background subtraction method needs extra processing time to minimize the effect of non-static background objects, such as that of the moving leaves of the trees, and to reduce the effect of the general camera noise that blurs the image before the background subtraction stage (Coifman et al., 1998; Cheung and Kamath, 2004; Milla et al., 2010; Barrero et al., 2010). Therefore, many challenges in developing a good background subtraction algorithm have been elaborated by Chalidabhongse et al. (2003), Piccardi (2004) and Mandellos et al. (2011) as follows:

1. The background subtraction algorithm must be robust against changes in illumination;

2. The background subtraction algorithm should avoid detecting non-stationary background objects, such as moving leaves of the trees, rain, snow, and shadows casted by moving objects;

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3. The internal background model of the background subtraction algorithm should react quickly to background changes, such starting and stopping of vehicles.

The above reviews have been summarized in Table 2.1 in terms of the techniques used.

Table 2.1: Summary of the techniques used with traffic light systems in terms of ability and efficiency.

Technique Advantages Disadvantages

TOD

-Simple control technique. - It is a fixed cycle protocol; and - It uses manual survey data.

- Vehicles demands of an intersection are:

1. To be inconsistent, even within different times of a day;

2. To be susceptible to variation in different days due to unexpected events; and

3. To avoid changes since the latter leads to invalid optimized settings.

Knowledge-based system

and Fuzzy logic

-The basic advantages offered by such a system are the documentation of knowledge, intelligent decision support, self-learning, reasoning and explanation.

- It requires a big knowledge base; in addition to the quality of rules that will be a part of the decision-making process; and

- It uses non-visual sensors, which suffer from many drawbacks.

Petri-net

- it is simple and straightforward to model features like

precedence relation,

concurrency, conflict and a mutual exclusion of real-time system;

- the formal graphical representation provides a medium to visualize the complex system;

-It has a well-developed mathematical foundation;

Its analysis can be carried out to detect deadlock, overflow and irreversible situations, etc; and -Performance evaluation is

- It is not straightforward when modeling the notion of time;

-As the system evolves in its size and complexity, the state-space of the Petri net grows exponentially; a matter which makes it too difficult to be managed both graphically and analytically;

-Its control logic is hard-wired; and - It uses non-visual sensors, which suffer from many drawbacks.

23 possible through the mathematical analysis of the system model.

ENN

-less learning time.

-less memory consumption.

-Its learning and converges process are iterative; and

- It uses infrared sensors, which suffer from drawbacks.

Reinforcemen t learning

-it automatically learns to recognize complex patterns; and - It makes intelligent decisions based on the data.

- It is less stable due to the necessary explorations of the environment;

- It is often impossible to fully determine the current state; and - It uses non-visual sensors, which suffer from many drawbacks.

GA

- It can solve the optimization problem;

- It solves problems with multiple solutions;

-it can solve multi-dimensional, non-differential, non-continuous, and even non-parametrical problems;

-The structural genetic algorithm gives the possibility to solve the solution structures and solution parameter problems at the same time;

-Genetic algorithm is a method, which is very easy to understand.

Practically, it does not demand mathematical knowledge; and -It is easily transferred to the existing simulations and models.

-Certain optimization problems cannot be solved by means of genetic algorithms;

-There is no absolute assurance that a genetic algorithm will find a global optimum;

- It cannot ensure constant optimization response times;

-Genetic algorithm applications, which are performed in real time are limited;

and

- It uses non-visual sensors, which suffer from many drawbacks.

Computer vision

-It is more economical, tireless, and more precise compared to the previous approaches; and - The technique manages to determine the number of vehicles via using the existing computer vision approaches,

such as vehicle

detection/classification or the segmentation approach.

- It suffers from vehicles' overlapping problem, which obstructs many vehicles in the streets;

- Stooping or slow-moving vehicles will increase the effect of the vehicles overlapping problem;

- It is unable to work in the nighttime;

- The noisy objects problem (e.g. trees and pedestrians) will decrease the efficiency of the work because of the inaccuracy; and

- It needs extra-processing, such as:

object segmentation, object classification and vehicles tracking.

24 2.2 Associative Memory

Mehrotra et al.,(1996) and Ponce et al., (2010) mentioned that it is believed that human memory is stored in the form of complex interconnections among various neurons. Similarly, the artificial neural network simulating associative memory collects the stored pattern in the form of a memory or weight matrix, which helps generate an output that corresponds to a given input. Gurney (1997) and Mehrotra et al.,(1996) further stated that such a process is referred to as learning or storing the desired patterns while the retrieval or recall process is referred to as the generation of an output pattern.

Figure 2.1 shows a general block diagram of an associative memory performing an associative mapping between an input vector x and an output vector v (see Equation 2.1).

Figure 2.1: Block diagram of an associative memory.

(Kishan Mehrotra,et al., 1996).

 

x

 

2.1

M v

As shown in Figure 2.1 shows a map of vector x to vector v, in the pattern space and the output space , respectively, is formed by means of a transformation.

Operator M denotes a general nonlinear matrix-type operator, which has different meanings for different memory systems. The form of the operator, in fact, defines a specific system that needs to be carefully outlined for each type of memory whereas its structure reflects a specific neural memory paradigm. For dynamic memories, M also

In document Thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy (halaman 37-42)